Does Exceptional Human Longevity Come With High Cost of
Infertility?

Testing the Evolutionary Theories of Aging

Center on Aging, NORC and the University of Chicago, 1155 East 60th
Street, Chicago, IL 60637-2745, USA

1. Introduction

This introductory section is written as
an abstract summarizing the idea, methods, findings and conclusions of
the study. The purpose of this study is to test the prediction of
evolutionary theory of aging that human longevity comes with the cost
of impaired reproductive success (higher infertility rates, see [1]).
Our validation study is based on the analysis of particularly reliable
genealogical records for European aristocratic families. This dataset
is appealing to use for two reasons:
(1) it has high data accuracy and completeness;
(2) confounding effects of socio-economic status are minimized in this
socially elite group.
The dataset is comprised of 3,723 married women born in 1500-1875 and
belonging to the upper European nobility. Every case of childlessness
was cross-checked using at least two different sources. Data analyses
were based on logistic regression model using childlessness as a
dependent (outcome) variable, and woman lifespan, calendar year of
birth, age at marriage, husband's age at marriage and husband's
lifespan as dependent (predictor) variables. We found that woman's
exceptional longevity does not increase her chances to be infertile. It
appears that the previous reports of high infertility among
long-lived women (up to 50% of infertility rate, see [1]) are related
to data incompleteness, caused by under-reporting of children. Indeed,
data cross-checking revealed that at least in 32% cases the allegedly
'childless' women did in fact had children. Thus, the concept of heavy
infertility cost for human longevity is not supported by data, when
these data are carefully cross-checked, cleaned and reanalyzed.
Additional relevant information is available at our scientific website (http://longevity-science.org/).

2. The Importance of Data Quality Control

Previous analysis of childlessness
among aristocratic women1 was made on the assumption of data
completeness. However, when claims are made that many long-lived women
are childless [1], it is important to crosscheck the data and to make
sure that the lack of children is real, rather than caused by data
incompleteness.

An obvious step is to cross-check the
initial dataset with other data sources. For example, we examined 335
claims of childlessness in the Bloore's dataset used by Westendorp and
Kirkwood. When we cross-checked these claims with other professional
sources of data, we found that at least 107 allegedly childless women
(32%) did have children. Thus, at least 32% of childlessness claims
proved to be wrong ("false negative claims").

This example demonstrates that extreme
caution should be exercised when claims for common childlessness among
long-lived women are made. The incompleteness of genealogies can
itself generate a spurious increase in the prevalence of allegedly
childless women among those who live long lives. This happens because
children are often not mentioned in particularly obscure, side branches
of genealogical trees (remote relatives). It is also known that
long-lived people have more chances of being mentioned in incomplete
genealogies, because of their longer paper trail in various archives
generated during their long life. Thus, incompleteness of genealogies
generates two types of biases - underreporting of children and inflated
prevalence of long-lived people, thereby producing a spurious rise in
claimed childlessness with increased lifespan.

Incomplete reporting of children may
seriously affect and compromise scientific studies of human fertility.
For example, Westendorp and Kirkwood reported: "None of the six women
who were born before 1700 and who reached the exceptional age of 90
years and over had more than two children" [1,p.745]. Our data
cross-checking with other data sources revealed that in fact none but
one of these women had less than three children.

Among these six women was Antoinette de
Bourbon (1493-1583) who allegedly had only one child according to the
Bloore's database. Study of other data sources revealed that this
well-known person (grandmother of Mary Stuart, Queen of Scots) had as
many as twelve children! This fact is well known to professional
genealogists and is even reported in The Catholic Encyclopedia (Vol.
VII, House of Guise, Robert Appleton Company, 1909). Thus, if we
compute an average number of children for women lived 90-99 years with
corrected data for Antoinette de Bourbon alone, their average number of
progeny would be even higher than average number of progeny for
shorter-lived women.

This example demonstrates that
genealogical data should be carefully checked against multiple
genealogical and historical sources before using them in the scientific
studies and making strong conclusions.

3. Results and Discussion

This section describes the results
obtained with cross-checked, corrected data. Table 1 presents the
dependence of the frequency of childlessness as a function of women
lifespan (univariate analysis).

The data obtained by other researchers
are also presented in the same table for comparison. Note extremely
high proportion of childless women in data published by Westendorp and
Kirkwood [1]. On the other hand, German data [2] as well as our data
for aristocratic women are consistent with each other and do not
demonstrate any increase in childlessness for long-lived women. Our
estimates of childlessness also are consistent with estimates of
childlessness among the British Peerage reported by Thomas
Hollingsworth in his fundamental historical study [3].

Results presented in Table 1 were
obtained using univariate analyses, which do not take into account many
important explanatory variables. In order to avoid the omitted
variable bias and to study the true relationship between childlessness
and longevity, we need to take into account many other explanatory
variables, which influence infertility rate. Therefore we applied
multivariate logistic regression with childlessness as a dependent
binary variable and calendar year of birth, female age at marriage,
husband's age at marriage, female lifespan and husband's lifespan as
predictor variables.

The main result of our study is
presented in Figure 1. This figure shows odds of being childless as a
function of female lifespan, adjusted for other important confounding
variables. The odds of childlessness are particularly high, when
women's lifespan is too short (below age 30), which is not surprising.
What is really important is that the chances of being childless do not
demonstrate any increase for long-lived women (lifespan 90+ years).
This result confirms findings from our univariate analyses (Table 1) as
well as from other studies [2,4], which demonstrated that long-lived
women do not have higher rate of childlessness even when controlled for
other important confounding variables.

Our study does not support the previous
published claims that human longevity comes at a high cost of
infertility. This conclusion may have both theoretical significance
(testing some evolutionary theories of aging), as well as practical
implications for the future of life extension. It helps to relax
concerns over a question: "Is it morally acceptable to extend human
longevity at the cost of infertility?" Some authors already raised
their concerns on the unintended consequences of life span extension:
"... increasing longevity through genetic manipulation of the
mechanisms of aging raises deep biological and moral questions. These
questions should give us pause before we embark on the enterprise of
extending our lives" [5]. This study helps to alleviate some concerns
on these issues.

Acknowledgments

This study was
made possible thanks to a generous support from the National Institute
on Aging (NIH, USA), and a stimulating working environment at the
Center on Aging, NORC/University of Chicago. We would like to thank
members of the Science Advisory Board, SAB (http://www.scienceboard.net/)
for useful comments on our work made at the SAB discussion group.

Table 1. Proportion of childlessness by women's age at death.
Comparison of our dataset with similar data for the historical German
population [2] and data for the British aristocracy [1].

Age at Death,
years

Proportion of Childless
Women inDifferent Datasets

Gavrilovs
dataset on European upper nobility

Lycett et
al.,(2000)
German data

Westendorp and
Kirkwood (1998),
British aristocracy

20-29

0.17

0.15

0.39

30-39

0.10

0.08

0.26

40-49

0.14

0.08

0.31

50-59

0.13

0.11

0.28

60-69

0.12

0.09

0.33

70-79

0.10

0.09

0.31

80-89

0.15

0.10

0.45

90+

0.12

----

0.49

Figure
1. Childlessness odds ratio as a function of female lifespan.
Net effects are adjusted for female calendar year of birth, female age
at marriage, husband's lifespan, and husband's age at marriage.
Multivariate regression analysis of 3,723 European aristocratic
families.